Brittany Kaiser Quotes

While data can only tell you what has happened in the past, it can in some ways give you a sense of what might be of interest to an audience in the future.

We saw that our customers required help beyond the data sets they had and that they could benefit from a wider opinion. So we built SurveyMonkey Audience, and we've now got 4 million users who signed up to take surveys. Our clients can choose the demographic they want to hear from, and we can provide that sample.

Our polling methodology has gotten outdated, and, in fact, it's not really telling us what it needs to be telling us.

Everyone takes surveys. Whoever makes a statement about human behavior has engaged in a survey of some sort.

One of the things we did at PayPal was collaborative filtering and machine learning: looking at patterns of human behavior. We used it there to predict when people would try to cheat the system to get money. But you can predict pretty much any behavior with a certain amount of accuracy.

The people who are thinking most about big data right now are corporations and governments.

Big data has been used by human beings for a long time - just in bricks-and-mortar applications. Insurance and standardized tests are both examples of big data from before the Internet.

If your experiment needs statistics, you ought to have done a better experiment.

I really like the mission at SurveyMonkey, which is, we help people make better decisions. It's just a great thing.

There is a calculus, it turns out, for mastering our subconscious urges. For companies like Target, the exhaustive rendering of our conscious and unconscious patterns into data sets and algorithms has revolutionized what they know about us and, therefore, how precisely they can sell.

The possibilities for creation and insight are endless. We're constantly collecting more data, and it's starting to be very relevant to our lives.

The world is now awash in data and we can see consumers in a lot clearer ways.

I think you can have a ridiculously enormous and complex data set, but if you have the right tools and methodology then it's not a problem.

Bobby and I went through some old questionnaires about customer requirements for languages, then we compiled a new one and sent it out to a few dozen people we knew.

In genomics, there's a massive amount of information in which you can look for patterns and develop insights.

There will always be plenty of things to compute in the detailed affairs of millions of people doing complicated things.

I was interested in data mining, which means analyzing large amounts of data, discovering patterns and trends. At the same time, Larry started downloading the Web, which turns out to be the most interesting data you can possibly mine.

Analytics is not about sitting behind a computer and pushing enter and having it produce an answer.

The job of the data scientist is to ask the right questions.

People believe the best way to learn from the data is to have a hypothesis and then go check it, but the data is so complex that someone who is working with a data set will not know the most significant things to ask. That's a huge problem.

The premise and promise of Big Data is that there are no stories, only patterns; that the human preference for story is aligned with the human tendency for error; and that only through dislocations in scale - the scale of sample size and of time - will truth emerge.

The bigger a data set that you have, the more polls, the more surveys that you have that people undertake, the more accurate your models are going to be. That's just a fact of data science.

I'm an analytics girl.

Data science is the combination of analytics and the development of new algorithms.

I keep saying the sexy job in the next ten years will be statisticians. People think I'm joking, but who would've guessed that computer engineers would've been the sexy job of the 1990s?